MSc.SS Theses and Dissertations (2023)
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- ItemApplication of Hybrid seasonal ARIMA-GARCH Model in modelling and forecasting fertilizer prices in Kenya(Strathmore University, 2023) Okello, E. A.Volatility in fertilizer prices pose a huge risk to both farmers and suppliers. To manage fertilizer price volatility, a more efficient price risk management model is necessary. Stand alone models have been criticized for failing to capture the true market conditions by capturing only the unilateral information. Better outcomes have been credited to combined models, such time series models. Existing models have factored in variables such as natural gas, transport, volumes traded, crude oil prices, corn prices, ethanol, market concentration and regions. In this study, the port through which fertilizer is imported is taken into account while creating a Hybrid SARIMA-GARCH model, which is then used to anticipate pricing. Using RMSE, MAE, and MASE, the model’s predictive abilities were assessed. The findings of this study suggest that the best model for the port of Gulf is SARIMA models (1, 1, 0) (2, 1, 0)12, with an AIC = 997.53, and RMSE = 5.6015, and can efficiently capture the pricing behaviour in this port. In Yuzhny, Hybrid SARIMA (2, 1, 0) (2, 1, 0)12–GARCH (1, 1) turned out to be the best fit with AIC = 7.4389, RMSE = 7.5802, MAE=5.4797 and MASE=0.6885. The study concludes that the port through which fertilizer is imported has an effect on the price placed as each of the ports under study yielded a unique model. KEY WORDS: Nonlinear time series, Heteroscedasticity, SARIMA model, GARCH model, Hybrid SARIMA GARCH model, Ljung–Box test, Augmented Dickey Fuller test.
- ItemExamining Gaussian Mixture Models using clustering algorithms(Strathmore University, 2023) Oloo, J. M.Clustering is an important data mining technique for finding homogeneous and heterogeneous groups in a data set. Identifying these groups from a sales data-set is important for estimating demand for a specific range of products. This research carried out a detailed analysis of Gaussian Mixture Models by using the expectation-maximization method to find optimal clusters on a sales data-set. The method combines expectation-maximization algorithm with the agglomerative hierarchical clustering, resulting in an effective, iterative process for estimating the model’s parameters. In order to give accurate estimates for the ideal number of clusters, the expectation-maximization approach uses the hierarchical clustering to provide an initial guess for the algorithm. The goal is to boost sales performance of products sold by estimating demand and comparing sales over a particular period. The method segmented clients into groups with shared characteristics, such that customers within each subgroup could be offered products and promotions that are likely to interest them. Therefore, this study was interested in maximizing the distance between individual clusters and also minimizing the distance between items belonging to the same cluster. The research experimented with sales data from a large liquor distribution company, examining how variables such as product, customer, sales region, and quantity sold affected overall sales volume and revenue. In order to identify deviation in product sales, the data-set was split into subsets. Also, before clustering and data pre-processing, exploratory data analysis was used to understand the features of the data. To correctly measure the performance of the clustering algorithm the study used the Bayesian Information Criterion as a goodness of fit metric. The results had two distinct clusters that represented analysis of 146 products and 223 customers from the dataset. These findings confirmed that Gaussian Mixture Models and EM algorithms are more effective at estimating the underlying key parameters and identifying subgroups of similar products and customers.
- ItemIdentifying the optimal time series model to predict Kenyan stock prices(Strathmore University, 2023) Moenga, P. K.Prior research indicates that a rise in the stock market has been associated with a correspond- ing upsurge in economic growth. The act of investing in stock prices serves to bolster a nation’s economy through the mobilization of long-term financial assets for the purpose of production, while simultaneously mitigating potential investment risks via diversification strategies. Hence, the significance of the stock market endures as government’s worldwide endeavor to achieve economic advancement as a primary objective. Investing in the stock market bears inherent risk due to the heightened levels of volatility and the intricate and capricious nature of the market. In order to make informed investment decisions, investors and market analysts must diligently analyze market behavior and formulate effective pur- chasing or selling strategies. One of the methods for comprehending the behavior of markets is by foreseeing impending values and possessing discernment with regard to the timing of investments. Investors have endeavored to devise various models that can precisely forecast the future values of stocks. This study aims to make a noteworthy contribution to the quest of forecasting stock prices for Kenyan companies by ascertaining the most optimal time series model. It employed the ARIMA and prophet model in order to ascertain the most suitable time series model for the prediction of share prices in Kenya. It has utilized the daily data of SAFARICOM PLC, Equity Group Holdings Limited (NSE: EQTY), KCB Group Limited (NSE: KCB), East African Breweries Limited (NSE: EABL) and Co-Operative Bank of Kenya Limited (NSE: COOP) for a period of five years, starting from January 2017 and ending in December 2021. The data set consisted of 1248 trading days, which were analyzed in the current investigation. The Root Mean Square Error (RMSE) was employed for model assessment in order to determine the optimal time series model for the prediction of stock prices. It discovered that the ARIMA model exhibited superior predictive performance in comparison with the Prophet model in forecasting Kenyan stock prices. The study posits that future research endeavors may benefit from augmenting sample size and encompassing multiple industries to improve the generalizability of findings.